Klasifikasi Citra Menggunakan Convolutional Neural Network dan K Fold Cross Validation

  • Ari Peryanto Universitas Ahmad Dahlan
  • Anton Yudhana Universitas Ahmad Dahlan
  • Rusydi Umar Universitas Ahmad Dahlan
Keywords: Convolution Neural Network, Image, Clasification, Cross Validation


Image classification is a fairly easy task for humans, but for machines it is something that is very complex and is a major problem in the field of Computer Vision which has long been sought for a solution. There are many algorithms used for image classification, one of which is Convolutional Neural Network, which is the development of Multi Layer Perceptron (MLP) and is one of the algorithms of Deep Learning. This method has the most significant results in image recognition, because this method tries to imitate the image recognition system in the human visual cortex, so it has the ability to process image information. In this research the implementation of this method is done by using the Keras library with the Python programming language. The results showed the percentage of accuracy with K = 5 cross-validation obtained the highest level of accuracy of 80.36% and the highest average accuracy of 76.49%, and system accuracy of 72.02%. For the lowest accuracy obtained in the 4th and 5th testing with an accuracy value of 66.07%. The system that has been made has also been able to predict with the highest average prediction of 60.31%, and the highest prediction value of 65.47%.


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How to Cite
A. Peryanto, A. Yudhana, and R. Umar, “Klasifikasi Citra Menggunakan Convolutional Neural Network dan K Fold Cross Validation”, JAIC, vol. 4, no. 1, pp. 45-51, May 2020.